Detecting Receptivity for mHealth Interventions in the Natural Environment

TL;DR

This study developed and tested machine-learning models deployed within a digital health coach to detect user receptivity in real-world settings, significantly improving timely intervention delivery for health behavior support.

Contribution

It introduces static and adaptive machine-learning models for real-time detection of receptivity in natural environments, advancing personalized mHealth interventions.

Findings

Machine-learning models improved receptivity detection by up to 40% over random timing.

Adaptive models showed increased receptivity over the course of the study.

Models effectively used contextual data to predict user receptivity.

Abstract

JITAI is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user's receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach~-- Ally~-- that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally~app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a \textit{static model\/} that was built before the study started and remained constant for all participants and an \textit{adaptive model\/} that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a \textit{control model\/} that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40\% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study.